The present invention relates to systems that use rules and profiles to determine a score that can be correlated with a risk that an event will occur, for example automated underwriting systems that determine overall risk of loss, and to the integration of this type of system in sales and marketing, for example of insurance, loans, and credit cards.
Risk engines are automated systems or computer programs that return a risk factor or score based on data. In the context of insurance, a risk engine may be an underwriting computer program that will return a risk factor or score that correlates with a probability of loss for the insurer, based on facts about the insured person or property. The objective of underwriting is to ensure that a risk is placed in the proper rate plan/tier and that appropriate credits are given. Three alternative approaches are generally considered when automating an underwriting process: (1) scoring, a mathematical method that applies a numerical score based on specific attributes; (2) profiling, a scenario-based method that compares a collection of attributes to predefined scenarios, where each scenario has an associated score or risk; and (3) building a neural network, a form of artificial intelligence that develops its own model to make underwriting decisions by training.
Scoring methods are known, and are excellent predictors of performance. However, because each individual fact is scored independently, the final decision may be difficult to explain to customers, the method may be difficult to develop, and may be imprecise or inconsistent.
Neural network methods are also known, and are very precise and consistent. However, because the details of the underwriting decision are developed through training, the final decision is almost impossible to explain to customers. Furthermore, training may be complex and slow, and performance prediction may be unreliable.
Profiling methods are known as well, and in contrast to scoring methods and neural network methods, they are very precise and consistent, and excellent predictors of performance. Furthermore, the final decision reached through profiling is easy to explain to customers, since each result corresponds to a specific scenario.
Risk engines that use a profiling method to underwrite personal automobile and property insurance, are known. Since customers of personal automobile and property insurance fall into a rather small number of scenarios, a profiling method for underwriting this type of insurance need not be vary sophisticated, and is easy to develop with known profile building computer programs. A need exists, however, for a business owner policy (BOP) underwriting system. A BOP underwriting system needs to be much more sophisticated than personal automobile and property insurance underwriting systems, because a BOP insures business risk, property risk associated with each business location, and the general liability risk of each business location.
Risk engines for underwriting have been implemented in a commercial setting to provide very fast underwriting decision. For example, in one implementation, any agent telephones an underwriter, who then uses a risk engine to make a quick underwriting decision for a policy from one insurance provider. In another implementation, specific pre-selected insurance agents (captive agents) are given direct access to a risk engine via a private computer network, to make quick underwriting decisions from one insurance provider. There exists a need, however, for an implementation that will allow any agent, or even customers, direct access to a risk engine, for a quick underwriting decision for a policy from multiple insurance providers.